Explore the recent global developments with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## ── Attaching packages ────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2     ✓ purrr   0.3.4
## ✓ tibble  3.0.3     ✓ dplyr   1.0.2
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Look at the data

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

unique(gapminder$year)
##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

We see an interesting spread with an outlier to the right. Answer the following questions, please:

Q1. Why does it make sense to have a log10 scale on x axis?

It’s because there is a huge amount of data. Log10 is the most reasonable way to visualize big amounts of data

Q2. What country is the richest in 1952 (far right on x axis)?

#You can use the filter, select and arrange-function:

gapminder %>% 
filter(year == 1952) %>% #filtering the data corresponding to 1952
select(country,gdpPercap) %>% #selecting the data
arrange(desc(gdpPercap)) #arranging it in the right order
## # A tibble: 142 x 2
##    country        gdpPercap
##    <fct>              <dbl>
##  1 Kuwait           108382.
##  2 Switzerland       14734.
##  3 United States     13990.
##  4 Canada            11367.
##  5 New Zealand       10557.
##  6 Norway            10095.
##  7 Australia         10040.
##  8 United Kingdom     9980.
##  9 Bahrain            9867.
## 10 Denmark            9692.
## # … with 132 more rows

#The tabel shows that Kuwait is the richest country in 1952

You can generate a similar plot for 2007 and compare the differences

theme_set(theme_bw())
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Q3. Can you differentiate the continents by color and fix the axis labels?

#I added the aes(color)-function within the geompoint, and by doing that the continents got different colours. Furthermore I used the labs-function in order to give the axis new labels

theme_set(theme_bw())
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color = continent)) +
  scale_x_log10() %>%
  labs(x= "GDP per capita", y= "Expected lifetime")

Q4. What are the five richest countries in the world in 2007?

gapminder %>% 
filter(year == 2007) %>% #filtering the data corresponding to 1952
select(country,gdpPercap) %>% #selecting the data
arrange(desc(gdpPercap)) #arranging it in the right order
## # A tibble: 142 x 2
##    country          gdpPercap
##    <fct>                <dbl>
##  1 Norway              49357.
##  2 Kuwait              47307.
##  3 Singapore           47143.
##  4 United States       42952.
##  5 Ireland             40676.
##  6 Hong Kong, China    39725.
##  7 Switzerland         37506.
##  8 Netherlands         36798.
##  9 Canada              36319.
## 10 Iceland             36181.
## # … with 132 more rows

The five richest countries in 2007 are Norway, Kuwait, Singapore, United States and Ireland

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

theme_set(theme_bw())
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(color = continent)) +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]

I use the labs-function to add a title that is the years to visualize the development in gdpPercap over the years

theme_set(theme_bw())
anim2 +
  labs(title = "year: {frame_time}") #labs is labels

Q6 Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]

Yes, by disabling the scientific quotation I made whole numbers

options(scipen = 999) #options relates to how R visualize it's data

Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

QUESTION

Can you show the development in Denmark’s life expectancy throughout the years?

ANSWER

DK <- gapminder %>% 
  filter(country == "Denmark")
ggplot(DK, aes (x = year, y = lifeExp, group = country))+ geom_line() +
 transition_reveal(year)

#The data shows that people are getting older and older i Denmark